MindSpeaking

We've trained 15,000+ people on Data Storytelling. 
Let's talk about training for your team — or your entire organization:

Founder & Trainer

Author of the bestselling book: People Skills for Analytical Thinkers

The Essential Workshops

Member Of The Board Of Advisors

Capgemini

•	Combining Behavioral Science & Data Analytics to improve customer experiences

Managing Consultant - Data & Digital Analytics

•	Lead of Digital Marketing & Analytics capability in the Netherlands 
•	Coach for 8 consultants who reported to me

Senior Consultant

Capgemini Academy

Trained 300+ people on topics like: 
• Storytelling
• Presentation skills

Trainer

Personal development

Traveling, Spanish classes, and writing a book.

Sabbatical for 6 months

Cognizant

Trainer - Interpersonal Skills

Consultant - Digital, Data & Analytics

Junior Consultant - Digital, Data & Analytics

Maastricht University

Taught the course Behavioral Economics to second-year Economics students.

Student Assistant, Behavioral Economics

Gazprom Marketing & Trading

Marketing Intelligence Internship

Research Project Maastricht

•	Conducted contractual market research for multinationals in Brazil for 4 months;
•	Trained by consultancy firms (BCG, KPMG, McKinsey).

Market Researcher - Brazil

Student bar De Uni

President

Data Storytelling & stakeholder communication training | MindSpeaking | The Human Side of Data

Many Data professionals are brilliant.

Their analysis? Razor-sharp.
Their models? Sophisticated.
Their attention to detail? Exceptional.

But translating insights into a story that non-technical stakeholders understand…
 
That’s a different ball game.

In 2015, I presented insights to a group of Marketeers. 

I expected: 
Applause.
Approval.
Action.

But I was wrong. 
Nothing got implemented. 

That’s when I learned the hard truth: technical skills don't drive impact—communication does.

🔹 Stakeholders don’t care about dashboards. They care about decisions.
🔹 Insights mean nothing — until people see why they matter.
🔹 Numbers get forgotten. Stories get remembered.

That’s why I founded MindSpeaking in 2020.
Since then, we’ve helped 15,000+ Data professionals transform from data providers to trusted business partners.

🚀 𝗧𝗥𝗔𝗜𝗡𝗜𝗡𝗚 𝗙𝗢𝗥 𝗬𝗢𝗨𝗥 𝗧𝗘𝗔𝗠
Want your team to move from presenting numbers to influencing decisions?
Let's talk: 
MindSpeaking.com/companytraining



Free Resources: 

📙  𝗕𝗢𝗢𝗞
People Skills for Analytical Thinkers 
#1 bestseller — read a free preview:
▪️ MindSpeaking.com/book

🧠  𝗙𝗥𝗘𝗘 𝗘𝗠𝗔𝗜𝗟 𝗖𝗢𝗨𝗥𝗦𝗘
Want to turn Data into compelling stories? 
Join 4,100 Data pros and take my free email course (only 2 min/day): 
▪️ www.mindspeaking.com/datastorytelling 

📊  𝗙𝗥𝗘𝗘 𝗦𝗘𝗟𝗙-𝗧𝗘𝗦𝗧
How skilled are you at Data Storytelling?
Take the free self-assessment (5 min) and grow to the next level:
▪️ MindSpeaking.com/maturity-model

Gilbert Eijkelenboom

Stop dumping data. Start telling a story. 
Here are 4 tips:

Most data professionals think their job is to present numbers.

But numbers alone don’t create impact. 

If stakeholders don’t get the message instantly, they’ll tune out.

Before: Data Dump
❌ Lots of colors → No clear focus. 
❌ Just raw numbers → No clear message.
❌ No direct labeling → Hard to read values.
❌ Stakeholders confused → “What’s the takeaway?”

After: Simple Storytelling
✅ Fewer colors → Only for emphasis.
✅ A clear title → Explains the insight.
✅ Simple annotation → Highlights the key change.
✅ Direct labeling → No guessing, numbers are clear.

If people have to decode your charts, you've lost them.

Be the guide. 
Tell people where to look. 

Agree? Share your #data visualization tips below.

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Data Storytelling training | Bestselling author: People Skills for Analytical Thinkers | Founder of MindSpeaking | Building the Human Side of Data

🤬 Data pros…
 Heard any of these before?

“Can you export to Excel?”
“Make the data support my decision.”
“I need it today.”

Happens.
A lot.

Here’s how to push back without friction:

1. Ask the “hidden question”
 ↳ Stakeholders often ask for a solution instead of the problem.
 ↳ Say: “What decision will this help you make?”
 ↳ This shifts the conversation instantly.

2. Reframe urgency
 ↳ “I need it today” usually means “I’m under pressure.”
 ↳ Respond with: “I can give you X today, Y tomorrow. Which helps you most?”

3. Use the “menu method”
 ↳ People love options.
 ↳ Offer 2–3 clear paths with trade-offs.
 ↳ It reduces endless back-and-forth and builds trust fast.

4. Translate gut feelings
 ↳ When someone says “the data doesn’t fit my gut”…
 ↳ Ask: “What outcome were you expecting?”
 ↳ Now you can compare reality vs. expectation.

Do you hear these lines often?

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Most trainings feel great in the room.
Two weeks later? Nothing sticks.

That’s not you. That’s your brain… roasted.

One-off training dumps everything at once.
Feels useful in the moment.
But then you get back to work.
Back-to-back meetings.
200 emails waiting.

And all those “new skills”?
Gone.
That’s why lasting change needs a different recipe.

Here’s how we build communication skills that stick for data professionals:

1. Time in between workshops
↳ Spaced repetition beats cramming.
↳ Spread learning over days, not one long session.

2. Learn > Apply > Learn > Apply
↳ Theory alone fades. Practice makes it stick.
↳ Learn a little. Then apply it in your work.

3. Specific for the audience
↳ We practice with your real stakeholder challenges.
↳ That’s how skills transfer to your day-to-day.

That’s how habits form.
That’s how skills stick.

That’s how Data professionals stop being seen as order takers.
And start being seen as trusted partners.

♻️ Liked it? Repost to your network.
👉 Follow Gilbert Eijkelenboom to learn Data Storytelling skills that stick.

JSESSIONID

Feed | LinkedIn

li_at

linkedin.com

🪦 RIP Dashboards: 5 Psychology Hacks to Stop Your Work from Dying

You spent weeks building it.
Stakeholders said, “Looks great!”

And then… silence.

❌ No one opens it.
❌ No decisions.
❌ No impact.

Your work deserves to be used.

Here’s how to make sure your insights don't die 👇

1. The Think-Aloud Protocol
🔹 Stop asking, “Is this useful?” people don’t know. Instead:
🔹 Hand them the dashboard. Let them talk out loud as they explore. 
🔹 Watch where they click, pause, and get stuck, this reveals problems.

💡 Usability expert Jakob Nielsen: 
“Users will never tell you what’s wrong. But they’ll show you.”

2. The IKEA Effect
🔹 Ask early: “What do you think?”
🔹 Show them drafts, not just the final version.
🔹 Let them tweak a few things, it makes them feel ownership.

💡 The more they’re involved, the more they’ll use it.

3. Design Thinking: Build for How They Work (Not What They Ask For)
🔹 Watch how they work today, where’s the friction?
🔹 Prototype early. Refine fast. 
🔹 Don’t spend weeks working on the wrong thing.

💡 If they struggle to use it, it’s not them, it’s the design.

4. Hick’s Law: Simplify or Die
🔹 Cut 20% of elements right now.
🔹 Highlight one key insight they should act on.
🔹 Pre-set defaults instead of making them tweak everything.

💡 Less friction = faster decisions.

5. The “Last Mile” Problem: Put Insights Where They’ll Be Seen
🔹 Push critical insights where people already work (Slack, email, CRM).
🔹 Example: Sales gets an alert when revenue drops below target.
🔹 Meet them where they already work, so data doesn’t get ignored.

💡 Make insights part of their workflow, not an extra step.

In short:
- Make it simple.
- Make them part of the process.
- Make sure it drives action.

Ever built something that no one used? 

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🚨 BREAKING NEWS 
“Urgent data request! Stakeholder needs it TODAY!”

The newsroom goes wild.
Data teams scramble.
Dashboards, reports, insights, rushed out at record speed.

Mission accomplished.

Three weeks later…

Silence.

Nobody touched the report.
Not even once.

Why?

Because urgency ≠ importance.
Because half the time, stakeholders don’t even know what they’ll do with the data.
Because “ASAP” often means “I just want it off my plate.”

And yet, we drop everything.
Again and again.

Here’s the thing, data isn’t breaking news.
It’s decision fuel.

So next time?
Push back.
Ask what decision this will drive.
Ask when they actually need it.
Ask if it’s worth dropping everything else.

If they don’t have a clear answer?
It’s not urgent.


Have you received many urgent requests? 

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90% of Data Analysts miss this.
It’s how they talk to stakeholders.

Not the tools.
Not the tech.

The conversations.

Here’s the cheat sheet I wish every Analyst had:

📝 When a request is vague
 – What decision does this help you make?
 – If you had the answer tomorrow, what would change?
 – If we could only answer one thing, what matters most?

🚫 When you need to push back
 – How does this request influence a decision?
 – What happens if we don’t do this?
 – To move forward, I need [data/input/approval]. Can you provide that?

🎯 When aligning on goals
 – What does success look like to you?
 – Who will use this insight?
 – What outcome makes this worth it?

📊 When presenting insights
 – Here’s the key takeaway…
 – If you remember one thing, let it be this…
 – Here’s what this means for the business…

⚡ When stakeholders disagree
 – I hear you. What’s behind that concern?
 – Which part doesn’t match your experience?
 – Let’s explore where we see it differently.

🤝 When building trust
 – What are your priorities this quarter?
 – What’s your team’s biggest challenge?
 – What would make this a win for you?

Because great communication doesn’t start with answers.
 It starts with better questions.

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It’s 2015, and I step into a meeting room in Seoul 🇰🇷. The commercial director is Korean. He shakes hands with my Dutch colleagues, all in their 40s. I extend mine too, but with my babyface, I look like a 20-year-old who just came to take notes. He leaves me hanging.

In that moment, I feel invisible.

But after a minute I realize, it’s not about me. It’s more about cultural differences, assumptions, and expectations.

The next time a colleague snaps at you,
Don’t take it personally.

Chances are, it has more to do with them than with you.

Maybe they missed a promotion.
Maybe they had a bad night’s sleep.
Maybe they’re juggling too many deadlines.

Yes, we can set boundaries.

But here’s what I’ve learned:

The more we realize the world doesn’t revolve around us,
the easier work and life become.

Do you agree?

♻️ Repost to help fellow data professionals.
Follow Gilbert Eijkelenboom for tips on Data Storytelling.

Your dashboards need a diet.
Here’s how to trim the fat:

Many dashboards are stuffed.
Every metric. 
Every filter. 
Every chart.

Just in case.

But here’s the problem
Too much info = No focus.
No focus = No action.

A great dashboard is like a Michelin-star dish.
Balanced.
Curated.
Easy to digest.

The best data professionals know this.
They don’t dump data.
They craft a story.

Next time you build a dashboard…

Ask yourself:
What’s the main question end-users need answered?

Then serve that.

Have you seen this happen?

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#data #analytics #bi

#Data storytelling done right.
Here’s the step-by-step process to turn data points into a clear, action-driven story.

From table to takeaway:

Step 1: Define the goal
↳ Write the one decision you want to influence.

Step 2: Understand the audience
↳ What is the decision they want to make with this data?

Step 3: Extract the key message
↳ Scan the table. What’s the one thing relevant for my audience?

Step 4: Write the headline
↳ Turn the key message into a sentence on top.

Step 5: Choose the right visual
↳ Replace the table with a simple chart that fits the message
(comparison → bar, trend → line).

Step 6: Put everything in grey
↳ Set all bars, lines, axes, and labels to light grey.

Step 7: Highlight the key point in color
↳ Color only the critical bar or line segment
(e.g., Online in Asia = red, -8%).

Step 8: Label the insight on-chart
↳ Add a short callout: “Online -8% drives Asia decline.”

Step 9: Add the recommendation
↳ One sentence that tells them what to do next.

Step 10: Trim the noise
↳ Remove gridlines, extra decimals, duplicate legends.

Step 11: Sanity check the story
↳ Read the slide top-left to bottom-right. Does the path make sense?

Step 12: Test with a colleague
↳ 10-second test: “What’s the message?”

That’s it.
From table to takeaway.
From noise to narrative.

Question:
Which step do you like most?

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Stop speaking data.
Start speaking business.

Because here’s the thing:

When you say “accuracy improved to 85%”…
…your stakeholder doesn’t know what that means.

Will it save time?
Grow the business?
Make customers happy?

If they don’t see it, they won’t act.

The solution?
Translate data into business language.

Here’s how:

Example 1 — Model accuracy → Business
❌ “Our fraud detection model improved from 78% to 85%.”
✅ “700 more fraud cases caught, saving $2M annually.”

Example 2 — Operational data → Business
❌ “The average response time of our customer service chatbot decreased from 17 to 11 seconds.”
✅ “Customers now get answers 35% faster. That keeps them from dropping off and boosts retention.”

Example 3 — Marketing metrics → Business
❌ “Conversion for campaign B was 2.1%, compared to 1.4% on campaign A.”
✅ “Campaign B brought 3,000 extra people to visit our product page. That’s $450k in potential new revenue.”

That’s the shift.
From numbers.
To impact.

Do you think speaking business language is important?

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Data storytelling doesn’t start with slides.
It starts with a goal.

If you want people to value and use your insights…

Do THIS, before your meeting 👇

1. Write your goal in one sentence
↳ What decision or action do you want?
↳ Phrase it as: “After this meeting, I want them to…”

2. You’ll walk in with clarity. And so will they.
List 3 questions you’ll ask
↳ To uncover what they really need.
↳ Use open-ended ones: “What would success look like for you?”
Helps you steer the talk instead of reacting.

3. Prepare objections
↳ Where might they push back?
↳ Draft 1–2 plain-language answers.
Saves you from getting defensive or lost in technical detail.

4. Reach your goal
↳ If they remember one thing, what should it be?
Write that takeaway on a sticky note.
Keep it in sight.

That’s how to make your data story land.
Do you set your goal before your presentations?

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Most data presentations fail.
Too much detail.
Too much jargon.
No clear message.

Stakeholders tune out.

The problem?

You spend weeks on analysis.
But the slides kill the insight.

That’s why I made this:

Top 7 Data Presentation Mistakes
❌ Data dumping
❌ Buried insights
❌ Color overload
❌ No business link
❌ Weak chart titles
❌ Lost in translation
❌ No recommendation

✅ With fixes you can apply today.
Save it.
Share it.
Use it.

Because your insight is only as strong as your presentation.
👉 Which mistake do you see most often?

Stop giving stakeholders homework.

❌ What most Data professionals say:
“This slide shows all the metrics for Q1, split by channel and segment.”

What business people hear:
Homework.

The message?
Blurry.

The insight?
Lost.

Here’s the clear version:

🌎 Context
Overall, sales grew in Q1.

⚡ Problem
But when we look deeper, sales dropped 13% in Europe.
Especially online sales is underperforming.

➡️ Recommendation
I recommend reviewing the campaign strategy.

Clarity beats complexity.
Every time.

Do you think data storytelling is a must-have skill today?

Stakeholders keep asking for dashboards... but do they actually use them?
Don’t just serve more dashboards. Take these 3 steps instead.

For all new requests, use the 3 C’s:

1️⃣ Connection
Build trust first. Show you care about their needs.
↳ Acknowledge their request: "I see these insights are important to you."
↳ Set the tone: "Let’s dive deeper to discover how I can best help you."

2️⃣ Curiosity
Ask the right questions to uncover the real need.
↳ "What business problem are we solving?"
↳ "What does success look like?"
↳ "What decision are you trying to make?"

3️⃣ Clarity
Set clear expectations and push back when needed.
↳ Reframe their request to avoid endless rework:
↳ "Instead of tracking every KPI, let’s create a simple dashboard showing which channels drive the most revenue, so you can optimize your budget."

💡 Next time a stakeholder asks for a new dashboard...
Don’t just serve it right away. 
Start with the 3 C’s and deliver insights they’ll actually consume.

Can you relate? 
Have you ever seen dashboards go unused?

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Stakeholders aren’t analysts.
Don’t make them act like one.

❌ How not to present data:
“We analyzed revenue by channel in Q1.
First, we cleaned the data.
(…)
You’ll find all breakdowns for stores, e-commerce, and wholesale.”

✅ Data Storytelling:
“Revenue dropped 12% in Q1.

What we saw:
Offline sales down 20%.
Online sales up 8%.

Here’s the surprise:
Our customers tell us they are willing to spend more online.
But we see 30% drop at checkout.
That’s lost money.

We need to fix checkout.
Then two things happen:
→ Online grows faster.
→ We recover lost revenue.”

That’s the shift.
From analysis.
To action.

♻️ Repost to help fellow data professionals.
Follow Gilbert Eijkelenboom for tips on Data Storytelling.

😈 Data brain says: “Add more detail.”
👼 Business brain says: “Get to the point.”

Ever feel this?

Welcome to the inner war:
Data brain vs Business brain.

If you’ve got both, congrats.
But if you lead with the wrong one
You’ll lose your audience.

Here’s how to balance both 👇

1. Start with the impact
↳ Your audience’s brain is noisy
↳ Hook them in 1 line: “Sales dropped 28%, here’s why.”

2. Then give data as proof
↳ Now that they care, show the logic
↳ “Conversion rate fell after we removed the free trial.”

3. Keep details optional
↳ Add depth… but let them pull, don’t push
↳ “We’ve got a breakdown by channel if needed.”

You don’t have to choose sides.
Just pick the right one to speak first.
That’s the trick.

Who wins in your mind?
Data or Business brain?

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Most analysts don’t know how to persuade.
They share the numbers.
They show the charts.
Still… stakeholders don’t listen.

The problem?
Persuasion isn’t about giving more data.
It’s about making people feel heard.

That’s where the AVP Framework comes in:
👂 Ask
✅ Validate
🚀 Persuade

I put together a one-pager with a real example.
Save it.
Use it.

Because the analysts who build influence…
Are the ones who make the biggest impact.

Do you agree?

♻️ Repost to help fellow data professionals.
Follow Gilbert Eijkelenboom for tips on Data Storytelling.

I love this image.
Same outfit for 40 years.

In data teams…
People keep evolving their tech stack. New models.

New tools.
New frameworks.

But when it’s time to explain their insights…
they fall back on the same old patterns.

Too much detail.
Too much jargon.
Same beige sweater.

This makes our audience fall asleep.
And that’s why brilliant work still gets ignored.

We need to present our data so that stakeholders say:
Tell me more!

The best way to do that?
Storytelling.

👉 Follow Gilbert Eijkelenboom for more Data Storytelling tips.

Here's an example: 
> Do you ride a bicycle regularly? Tamara did too. But one day in 2018, that changed.

She biked to work, thinking about her next meeting.

Then, all of a sudden, a car appeared. 

The car hit Tamara from the side, her head smashing on the street. 

She got into a coma.

I sat beside her hospital bed, holding her hand.

Days passed by. 

Weeks passed by. 

Months passed by.

I thought she'd never wake up. 

After 6 months, she opened her eyes. Luckily, Tamara survived.

But until today, she's unable to read.

Tamara isn't an exception. 

Accidents with bicycles are on the rise. 

People with a helmet have 88% less risk of brain injury.

So when you go out on your bike today 

Wear a helmet.


Why is this more persuasive? 
✅ Helps the audience visualize
✅ Includes tension and emotion
✅ Brings the data to life by zooming in on one character
 
More tips: 

1. Bonus points for personal stories

2. Business story? Use customer personas.

3. Use cognitive biases to your advantage. Example: loss aversion. Instead of telling people what they will keep (safety), tell them what they risk losing (ability to read). 

4. Know your audience. Don't even try convincing Dutch people to wear a helmet 😉

Most data professionals stop at Earth View:

“Here’s everything. Good luck!”

But stakeholders don’t want the whole planet.
They want the street they’re standing on.

That’s the shift:

From showing all the data
→
to highlighting what matters now.

Data storytelling isn’t about more data.
It isn’t about more charts.
It’s about zooming in.

Get on the street.
Show them the red light.
Show them what’s urgent.

Because presenting data isn’t about informing.
It’s about influencing decisions.

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Shell Energy Australia

Accountable for driving the data management capabilities to empower stakeholders with trusted, well governed and secure data. Working with the Data and Analytics team to establish the framework, then work with business and technical stakeholders to identify and define critical data elements, implement data quality controls, and help embed effective data management activities across Shell Energy.

- Developing and implementing Data quality and governance frameworks in the data analytics platform
- Ensuring controls and processes are in place that balance the need for secure, well governed data with the need to deliver business value quickly.
- Educate the business on standards and best practice for data retention and consumption
Catalogue critical data elements
- Ensuring all relevant team members and stakeholders are informed on data governance frameworks and solutions
- Work with the Data and Analytics team to establish data owners/stewards across Shell Energy
- Work with the wider Shell group to align our governance practices.

Data Governance Analyst

Judo Bank

•	Improving data quality for making informed decisions to improve operation efficiency, reduce costs and increase customer satisfaction.
•	Increased Trust in data through defining critical data elements, establish data linage, implementing data quality checks. Delivering these to data generators, owners and users in easy-to-understand data visualisations.
•	Enhancing regulatory compliance through automating regulatory reporting to APRA, ACCC and internal audit units.
•	Reduced organisational data costs through cataloguing of operational and financial data. This allows Judo to prioritise data storage and streamline data management process. 

Key projects:

Repayment Recalculations Projects:
Together a team of 8 consisting of product managers, robotics process automation technician, operational servicing associates and core banking specialist. Identified, modelled, and build end to end data process that allowed automation process for processing interest rate increases on 70% of active loans portfolio.

Servicing management solution for operational team. 
•	Identify and monitor key performance metrics on services delivery. 
•	Management gain insights on key points to invest in process improvements. 
•	Bankers and customer received quicker turn around on service.
•	Identified and improved critical elements regulatory compliance reporting

Migration of and consolidation of semantic data layer to Power BI
•	Reducing licensing costs significantly
•	Reducing data storage costs significantly
•	Increasing accessibility, security and trust in data and insights provided

Data Insights Analyst / Power BI Administrator

Sustainability Victoria

•	Respond to requests to provide analyses and visualisations to government, industry, SV and other clients and stakeholders.
•	Develop solutions derived from multiple data sources using a range of tools to deliver data that enable insight and decision-making. 
•	Develop areas of insight in consultation with internal and external clients and stakeholders, and prepare visually compelling presentations, information products and visualisations in line with the agrees range of products.
•	Translate the needs of stakeholders and provide insights in formats to suit the required purpose.
•	Prepare written reports, fact sheets and web-based information products with insights, analysis and visualisations.
- Together with the team received SV's Impact Award in 2021 for delivering and implemeting a combined market intelligence and data forecasting capacity for recycled material flow.

Data Analyst

HeadUp Labs

Delivered complete end to end machine learning prediction models for number of sick days based on biometrics data gathered from sports wearables such as Fitbit and Garmin.
 - Utilising Python,  PySpark and Databricks in Microsoft Azure to build ETL piplines to gather cloud data.
 - End to end pipline build using Azure Data Factory and Databricks
Best model was created 97% accuracy and 89% auc on test data.

Complete business solution was well received in live video presentation to internal stakeholders with clear explaination of underlying concepts and assumptions.

Data Scientist

Downer

Utilized Machine Learning in Python to provide predictions to predict road defect levels to an accuracy of 76%.
Scraped data and build dashboards in Power BI for market analysis of product sales across Australian local government areas from legacy data systems.

Self Employed

Own businesses in health and fitness
•	Presentation of products / services across a variety of mediums
•	Designed and implemented marketing campaigns both digital and print

Church Street Health and Fitness Club

Independent fitness consultant in Brighton.
Transferrable skills including
•	Communication skills developed from delivering over 10,000+ training sessions
•	Planning and creative thinking from designing engaging programs that deliver results.
•	Marketing campaign design, plan and implementation.

Fitness Consultant

Australian Public Service

Key accomplishment: 
Was able to improve report delivery time in the International Education Group by +300%

Main Roles:
Data visualization on financial status of key deliverable projects.
Collected, cleaned and analyzed financial data on work flow trends.
Provided monthly reports, data visualization and forecasts on the key deliverables
Financial records management for capital budget and expatriate staff budget management.

Senior Financial Reporting Analyst

I am a Data Governance and Power BI expert with a focus on leveraging statistical techniques and machine learning to help businesses get the best value from their data.  I help organisations build trust in the data so they can make better data driven business decisions as well as uncover hidden opportunities within their organisation.  I can take business ideas and translate them into successful data science projects to deliver the right insights.

So why Data Analytics? For me, there is nothing more mind-numbing than tedious, repetitive work. That’s why I love creating efficiencies through automation. Ones that can really impact people’s lives so they spend more time on critical problem solving activities and which will make a difference to their business. 

Additionally, I am passionate about amplifying the value that data can deliver for an organisation, giving them access to real time performance indicators and more accurate predictions so that they can better identify trends in key performance indicators that were not previously visible.

I possess demonstrable experience in the creation of effective data pipelines with the ability to extract and/or create appropriate data features for reports, visualisations, and predictions.   Additionally, I can construct comprehensive forecast and prediction models utilising latest machine learning algorithms and model building techniques.   I have successfully delivered data science projects using SQL, Python, R, Databricks, Power BI, Azure ML, Azure Data Factory, Databricks and Microsoft Excel.


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Gilbert Eijkelenboom

Chris Y. Z.

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taplio

The Battle

Gilbert Eijkelenboom

Chris Y. Z.

Followers

Posts

Engagements

Likes

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Gilbert Eijkelenboom's LinkedIn Performance

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Performance ScoreGilbert Eijkelenboom's LinkedIn Performance